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Automated Lung Nodule Segmentation Using Dynamic Programming and EM Based Classification

Summary: Automated Lung Nodule Segmentation Using Dynamic
Programming and EM Based Classification
Ning Xua, Narendra Ahujaa and Ravi Bansalb
aECE Department and Beckman Institute, University of Illinois at Urbana-Champaign, IL 61801
bSiemens Corporate Research, Inc. Princeton, NJ 08540
In this paper we present a robust and automated algorithm to segment lung nodules in three dimensional (3D)
Computed Tomography (CT) volume dataset. The nodule is segmented out in slice­per­slice basis, that is, we
first process each CT slice separately to extract two dimensional (2D) contours of the nodule which can then
be stacked together to get the whole 3D surface. The extracted 2D contours are optimal as we utilize dynamic
programming based optimization algorithm. To extract each 2D contour, we utilize a shape based constraint.
Given a physician specified point on the nodule, we blow a circle which gives us rough initialization of the
nodule from where our dynamic programming based algorithm estimates the optimal contour. As a nodule can
be calcified, we pre­process a small region­of­interest (ROI), around the physician selected point on the nodule
boundary, using the Expectation Maximization (EM) based algorithm to classify and remove calcification. Our
proposed approach can be consistently and robustly used to segment not only the solitary nodules but also the
nodules attached to lung walls and vessels.
Keywords: Lung nodule, Segmentation, Dynamic Programming, Expectation Maximization, Calcification


Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign


Collections: Computer Technologies and Information Sciences; Engineering